MultiBBQ: A Fairness Benchmark for Multimodal LLMs

Controllable diagnosis of social bias in multimodal LLMs with synthetic images.

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Updates

• [Jul 2026] Initial release of MultiBBQ: the dataset, the evaluation toolkit, and the Fairness/Bias/Unknown-rate scoring package.
• [Jul 2026] Paper online: Fairness Failure Modes of Multimodal LLMs, by Canyu Chen*, Anglin Cai*, Joan Nwatu, Yale Li, Jessica Hullman, Rada Mihalcea, Kathleen McKeown, and Manling Li (Northwestern / Columbia / Michigan / Illinois Tech; *equal contribution). This work is honored to receive the 🏆 Best Paper Award in the ACL 2026 Workshop on Trustworthy Natural Language Processing.

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Overview

An MLLM can pick up social bias not only from what it reads but also from what it sees. MultiBBQ measures the visual side: it extends the language-only BBQ benchmark into the multimodal domain by pairing each attested social bias with an AI-generated photorealistic image of two people who differ only in the target demographic (same setting, clothing, and other attributes). The dataset covers 410 examples / 2,460 question-answer pairs across four visually identifiable categories: Gender, Race, Religion, and Age.

Each example is evaluated under three scenarios, and fair behavior is well defined in each:

Scenario	The model sees	Fair behavior (counted by FS)	Biased behavior (counted by BS)
Visual-Only, Ambiguous	image only	answer Unknown: the image alone supports neither person	pick the stereotype-aligned person
Visual-Language, Ambiguous	image + under-informative context	answer Unknown	pick the stereotype-aligned person
Visual-Language, Disambiguated	image + context that determines the answer	pick the evidence-backed person, whether or not that aligns with the stereotype	when the correct answer contradicts the stereotype (the counter-bias subset), still pick the stereotype-aligned person: the stereotype overrides the evidence

The Fairness Score is the rate of fair answers and the Bias Score the rate of biased answers, each over a denominator fixed by ground truth. They are complementary rather than mirror images: in an ambiguous context, picking the non-stereotyped person is unfair (it lowers FS) but not biased (it does not raise BS).

How each piece works, in depth: the record schema and image manifest in docs/benchmark/dataset.md, how these behaviors are scored in docs/benchmark/metrics.md, and how the text and images were built in docs/benchmark/dataset-construction.md.

This repository has two sides. The evaluation toolkit is meant to be reused: score any vision-language model or text-only LLM for social bias with two complementary metrics on a controllable dataset. The benchmark study is the specific evaluation we report in the paper, where we run 28 models across the main benchmark and its factor, robustness, and mitigation studies, and diagnose four Fairness Failure Modes. (The toolkit also ships a few extension settings the paper does not report, marked as such in docs/benchmark/experiments.md.)

Key designs

Three design choices, mirrored from the paper, make the measured numbers attributable to bias rather than to artifacts:

• Shortcut Mitigation. MLLMs tend to over-rely on text and neglect the image. If the question or options contain demographic terms ("the man", "the woman"), a model can pick the correct answer from language alone, without ever reasoning over the image, and the benchmark stops measuring visual bias. MultiBBQ therefore replaces demographic terms with positional references ("the person on the left / right") that only the image can resolve, enforcing cross-modal reasoning. Disambiguated contexts deliberately keep their demographic terms: there, mapping the description to a position still requires the image. Option order and the stereotype / non-stereotype assignment are randomized to remove position and ordering shortcuts.
• Controllable image synthesis. Synthetic images make each pair a controlled experiment (only the target demographic differs), avoid training-data contamination, and involve no real individuals. Every image passed a four-rater, all-pass quality filter for Identifiability (the demographic is visually recognizable), Faithfulness (the image matches its context), and Controllability (nothing else differs). Two independent generators back the validity: model rankings agree across GPT-Image-1 and Imagen 4 Ultra (Pearson r = 0.9963 on FS_Total) and transfer to real face images (r = 0.9787).
• Metrics that separate abstention from bias and are anti-gaming. BBQ's original scores are computed over non-Unknown responses only, which conflates how often a model abstains with how biased it is when it answers: a model that abstains on 95% of questions but is fully bias-aligned on the rest produces the same number of biased outputs as one that rarely abstains and is biased on ~5% of its answers, yet BBQ assigns the former a bias magnitude 16x smaller because its abstention masks the underlying tendency. Our proposed Fairness Score (FS, higher is better) and Bias Score (BS, lower is better) use fixed, ground-truth-defined denominators, so together they expose the degenerate strategies (always-abstain, always-stereotype, always-anti-stereotype) that fool single-score benchmarks. See docs/benchmark/metrics.md.

Evaluation toolkit (reusable)

Capabilities you can apply to your own models and studies:

• Evaluate both Visual-Language Models and Text-only LLMs. MultiBBQ is built on BBQ, so every record also carries the full language-only question. Score a multimodal model on images, or a text-only LLM on the text alone, with the same pipeline and metrics. See docs/extending/llm-evaluation.md.
• Two complementary metrics. Fairness Score and Bias Score (plus Unknown-rate), with the full scoring pipeline included; see Key designs above and docs/benchmark/metrics.md.
• Controllable dataset. Every record is a minimal pair with shortcut-mitigated text and QC-filtered images (see Key designs), shipped in both masked (positional) and unmasked (BBQ-style) forms. See docs/benchmark/dataset.md.
• One command. multibbq run / score / combine / aggregate / pipeline drives inference and scoring end to end; each evaluation setting is a single --experiment flag.

Benchmark study (this paper)

The dimensions of the specific evaluation reported in the paper:

• 28 models across 11 families. 6 proprietary (GPT-4o, GPT-5 base/mini/nano, Gemini 2.5 flash/flash-lite) and 22 open-source (InternVL3.5, Qwen2.5-VL, LLaVA-1.6, Gemma3, MiniCPM-V, DeepSeek-VL, BLIP-2, Fuyu). See docs/benchmark/models.md.
• 11 evaluation settings. Baseline, image perturbation, quantization, decoding temperature, reasoning and fairness-instruction mitigation, real-world images, backbone (unmasked) studies, and text-only LLM evaluation. See docs/benchmark/experiments.md.
• Four Fairness Failure Modes. (1) Divergent failures between proprietary and open-source models (proprietary over-refuse in disambiguated contexts; open-source fail to abstain in ambiguous ones). (2) Fairness degradation under input (image noise) and model (quantization) factors. (3) Bias amplification over the backbone LLM. (4) Limited, inconsistent effect of mitigation.

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Repository layout

MultiBBQ/
├── README.md                 # this file
├── LICENSE                   # MIT (code); dataset is CC-BY-4.0
├── pyproject.toml            # the `multibbq` package + CLI
├── environment.yml           # conda env `multibbq` (model backends)
├── multibbq/                 # the package (CLI, inference, models, metrics)
├── data/                     # the dataset, one subfolder per role
│   ├── metadata/             #   evaluation record tables (what the harness reads)
│   ├── images/               #   image trees, laid out by `multibbq download`
│   ├── images_sample/        #   small browsable preview
│   └── construction/         #   templates, vocabulary, build tables (provenance)
├── scripts/                  # Slurm and bash launchers (one per experiment)
├── notebooks/                # dataset and image generation (provenance)
├── docs/                     # user-facing documentation (see below)
└── assets/                   # logo, figures

Per-file code map: multibbq/README.md.

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Installation

MultiBBQ runs on Python 3.9+. Model backends (torch, transformers, and model-specific libraries) are heavy and hardware-dependent, so they install via conda; the package itself and its metric pipeline are a light pip install.

# 1. model backends, into conda env `multibbq`
conda env create -f environment.yml
conda activate multibbq

# 2. the multibbq package + CLI
pip install -e .

The metric subcommands need only pandas, so scoring works on a GPU-free machine from a plain pip install -e .. Full setup, including API keys (OpenAI, Vertex AI, HF) and dataset and image download, is in docs/getting-started/installation.md.

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Quick Start (step by step)

1. Install (above), then set any API keys you need:

export OPENAI_API_KEY=sk-...            # GPT-4o / GPT-5
export GOOGLE_CLOUD_PROJECT=my-project  # Gemini (Vertex AI)

2. Get the images. Inference reads images from ./data/images/ (relative to where you run). Pull the released image set from the HuggingFace Hub, which lays them out for you:

pip install -e ".[hf]"
multibbq download                     # main image set -> ./data/images/   (~2.7 GB)

That is enough for every experiment except three: add --realworld (~130 MB) for the realworld experiment and --perturbations (~16 GB) for aug_img / img_label. The command is idempotent - re-run it to resume. The complete download guide (all methods, sizes, and where files land) is docs/getting-started/installation.md. To evaluate a text-only LLM you can skip this step, since the llm experiment uses no images.

3. Run inference on one model:

multibbq run "OpenGVLab/InternVL3_5-8B" --experiment main \
    --data_id gpt_image_gen --textual_context true --ambiguous true --negative true

This writes results/gpt_image_gen_main/OpenGVLab/InternVL3_5-8B/...json. To sweep all six context-by-question conditions and many models, use the batch launchers:

sbatch scripts/eval_main.sh          # cluster (edit the resource header first)
bash   scripts/eval_main_cpu.sh      # local, no Slurm

4. Compute metrics:

multibbq pipeline --input results/gpt_image_gen_main --output analysis/gpt_image_gen_main

This scores every result file, combines them, and writes the per-category CSVs plus the FS_total / BS_total summary. See docs/getting-started/running.md and docs/benchmark/metrics.md.

Experiments

--experiment	Study	Extra flags
main	Baseline (gpt_image_gen / imagen4ultra_image_gen images)	(none)
aug_img	Image perturbation	--img_aug_type
img_label	Generic-label options	--img_aug_type label
quant	Model quantization	(none)
temp	Decoding temperature	--temperature
reasoning	Mitigation (reasoning / fairness instruction)	--reasoning_mode
realworld	Real-world images (visual-language only)	(none)
context_unmasked	Demographic names injected into context	(none)
unmasked_w_img / unmasked_wo_img	Backbone eval (unmasked text)	(none)
llm	Text-only LLM evaluation (no image)	(none)

Full map to paper sections: docs/benchmark/experiments.md.

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Reproducing the paper

There are two reproduction paths: use the released images (deterministic, recommended) or regenerate images from scratch (non-deterministic, since GPT-Image-1 and Imagen-4-Ultra sampling is not seed-stable). Both are written out step by step, per experiment, with model lists and cost notes, in docs/benchmark/reproducing.md.

All experiments use greedy decoding (temperature=0) except the temperature study, and were run on NVIDIA H100 GPUs.

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Documentation

Doc	Contents
docs/getting-started/installation.md	Conda env, pip install -e ., API-key table, dataset and image download.
docs/getting-started/running.md	The run, score, combine, aggregate, pipeline chain; flags; output layout; cost.
docs/benchmark/reproducing.md	Step-by-step reproduction, both image paths, per-experiment recipes.
docs/benchmark/experiments.md	The 11 evaluation settings mapped to paper sections and CLI flags.
docs/benchmark/metrics.md	Fairness / Bias / Unknown-rate definitions, formulas, and the scoring pipeline.
docs/benchmark/dataset.md	Dataset card, record schema, and image manifest.
docs/benchmark/dataset-construction.md	How the dataset and images were built, and how to regenerate them.
docs/benchmark/models.md	Supported models, ids, and download links.
docs/extending/evaluate-your-own-model.md	Add a new model or adapter to the benchmark.
docs/extending/llm-evaluation.md	Evaluating text-only LLMs on MultiBBQ.
docs/extending/extending.md	Add an experiment, a metric, or a bias category.
docs/huggingface/hf.md	HuggingFace layout: the dataset repo, multibbq download, and the outputs repo.
docs/benchmark/RESULTS.md	Where the images, raw results, and computed analysis live.

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Citation

If you use MultiBBQ in your research, please cite:

@article{chen2026multibbq,
  title   = {Fairness Failure Modes of Multimodal LLMs},
  author  = {Chen, Canyu and Cai, Anglin and Nwatu, Joan and Li, Yale and
             Hullman, Jessica and Mihalcea, Rada and McKeown, Kathleen and Li, Manling},
  year    = {2026},
  note    = {MultiBBQ. Project: https://multibbq.github.io},
}

License

• Code: MIT, see LICENSE.
• Dataset: CC-BY-4.0.

Acknowledgements

MultiBBQ builds on the BBQ bias benchmark: BBQ: A Hand-Built Bias Benchmark for Question Answering (Parrish et al., 2022), github.com/nyu-mll/BBQ. Images are generated with GPT-Image-1 (OpenAI) and Imagen 4 Ultra (Google), and real-face experiments use the Face Research Lab London Set. We thank the authors of the benchmarked open-source models (see docs/benchmark/models.md). Developed in the MLL Lab at Northwestern University.